Multi-Cause Effect Estimation with Disentangled Confounder Representation
Multi-Cause Effect Estimation with Disentangled Confounder Representation
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DOI：
10.24963/ijcai.2021/384
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Jing Ma;Ruocheng Guo;Aidong Zhang;Jundong Li
中科院分区：
文献类型：
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作者：
Jing Ma;Ruocheng Guo;Aidong Zhang;Jundong Li
因果关系学习中的一个基本问题是估计一种或多种治疗的因果效应（例如，处方中的药物）对重要结果的影响（例如，治愈一种疾病）。因果效应估计的一个主要挑战是存在未观察到的混杂因素-影响治疗和结果的未观察到的变量。最近的研究表明，通过建模如何将实例与不同的处理一起分配，可以通过其学习的潜在表征捕获未观察到的混淆因素的模式。然而，这些作品中的表现的可解释性是有限的。在本文中，我们专注于从一个新的角度来研究多因果估计问题，通过学习解纠缠表示的混杂因素。解纠缠的表征不仅有利于治疗效果的估计，而且加强了对因果关系学习过程的理解。在合成数据集和真实数据集上的实验结果从不同方面证明了该框架的优越性。
One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e.g., medicines in the prescription) on an important outcome (e.g., cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.